Small Sample Inferences on the Sharpe Ratio
© 2016 Taylor & Francis Group, LLC. This work deals with statistical inferences on the "Sharpe Ratio" (SR) based on small samples. We have considered point estimation, interval estimation, as well as hypothesis testing, assuming that a random sample is available from a normal distrib...
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th-mahidol.433082019-03-14T15:04:22Z Small Sample Inferences on the Sharpe Ratio Suntaree Unhapipat Jun Yu Chen Nabendu Pal Mahidol University Tamkang University University of Louisiana at Lafayette Business, Management and Accounting Mathematics © 2016 Taylor & Francis Group, LLC. This work deals with statistical inferences on the "Sharpe Ratio" (SR) based on small samples. We have considered point estimation, interval estimation, as well as hypothesis testing, assuming that a random sample is available from a normal distribution. Further, we study the robustness of our inferential methods when the data is thought to have come from other nonnormal distributions but is mistakenly modeled by the normal distribution. Results from a comprehensive simulation study have been provided to justify our observations and recommendations. Among other things, we have proposed a new estimator of SR that performs much better than the commonly used maximum likelihood estimator. Finally, some mutual fund datasets have been used for demonstration purposes to estimate SR in order to assess their monthly return performances. 2018-12-11T02:27:47Z 2019-03-14T08:04:22Z 2018-12-11T02:27:47Z 2019-03-14T08:04:22Z 2016-04-02 Article American Journal of Mathematical and Management Sciences. Vol.35, No.2 (2016), 105-123 10.1080/01966324.2015.1121847 01966324 2-s2.0-84960498281 https://repository.li.mahidol.ac.th/handle/123456789/43308 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84960498281&origin=inward |
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Business, Management and Accounting Mathematics Suntaree Unhapipat Jun Yu Chen Nabendu Pal Small Sample Inferences on the Sharpe Ratio |
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© 2016 Taylor & Francis Group, LLC. This work deals with statistical inferences on the "Sharpe Ratio" (SR) based on small samples. We have considered point estimation, interval estimation, as well as hypothesis testing, assuming that a random sample is available from a normal distribution. Further, we study the robustness of our inferential methods when the data is thought to have come from other nonnormal distributions but is mistakenly modeled by the normal distribution. Results from a comprehensive simulation study have been provided to justify our observations and recommendations. Among other things, we have proposed a new estimator of SR that performs much better than the commonly used maximum likelihood estimator. Finally, some mutual fund datasets have been used for demonstration purposes to estimate SR in order to assess their monthly return performances. |
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Mahidol University Suntaree Unhapipat Jun Yu Chen Nabendu Pal |
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Article |
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Suntaree Unhapipat Jun Yu Chen Nabendu Pal |
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Suntaree Unhapipat |
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Small Sample Inferences on the Sharpe Ratio |
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Small Sample Inferences on the Sharpe Ratio |
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Small Sample Inferences on the Sharpe Ratio |
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Small Sample Inferences on the Sharpe Ratio |
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Small Sample Inferences on the Sharpe Ratio |
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small sample inferences on the sharpe ratio |
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2018 |
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https://repository.li.mahidol.ac.th/handle/123456789/43308 |
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